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Programming Collective Intelligence: Building Smart Web 2.0 Applications | 
 enlarge | Author: Toby Segaran
Publisher: O'Reilly Media, Inc.
Category: Book
List Price: $39.99
Buy New: $22.83
You Save: $17.16 (43%)
New (33) Used (9) from $21.00
Rating:  34 reviews
Sales Rank: 1332
Format: Illustrated
Media: Paperback
Pages: 360
Number Of Items: 1
Shipping Weight (lbs): 1.3
Dimensions (in): 9.1 x 7 x 0.7
ISBN: 0596529325
Dewey Decimal Number: 006.76
EAN: 9780596529321
Publication Date: August 16, 2007
Availability: Usually ships in 1-2 business days
Condition: All orders ship same business day via standard shipping (USPS Media Mail) if received by 1 PM CST.
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Product Description
Want to tap the power behind search rankings, product recommendations, social bookmarking, and online matchmaking? This fascinating book demonstrates how you can build Web 2.0 applications to mine the enormous amount of data created by people on the Internet. With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it. Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains: Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in adataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details." -- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths." -- Tim Wolters, CTO, Collective Intellect
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| Customer Reviews: Read 29 more reviews...
 Understanding the logic behind sites like Amazon and Google... October 20, 2007
Thomas Duff (Portland, OR United States)
33 out of 34 found this review helpful
Have you ever wondered how some of those "collective intelligence" sites work? How Amazon can suggest books that you'll like based on your browsing history? How a search engine can rank and filter results? Toby Segaran does a very good job in revealing and teaching those types of algorithms in his book Programming Collective Intelligence: Building Smart Web 2.0 Applications. While I'm not ready to run out and build my own version of Facebook now, at least I can start to understand how sites like that are designed.
Contents:
Introduction to Collective Intelligence; Making Recommendations; Discovering Groups; Searching and Ranking; Optimization; Document Filtering; Modeling with Decision Trees; Building Price Models; Advanced Classification - Kernel Methods and SVMs; Finding Independent Features; Evolving Intelligence; Algorithm Summary; Third-Party Libraries; Mathematical Formulas; Index
In each of the chapters, Segaran takes a type of capability, be it decision-making or filtering, and shows how a programming language can be used to build that feature. His examples are all in Python, so it helps if you are already familiar with that language if you want to actually work with the code. But even if you don't know Python, the examples are clear and detailed enough that you can follow along and get the gist of what's happening. I personally think that it would help immensely if you had a background in mathematics and statistics. You can use the code here without having a detailed understanding of math, but I'm sure much of this would be more deeply appreciated if you already know about such things as Tanimoto similarity scores, Euclidean distances, or Pearson coefficients.
From my perspective (a non-Python programmer *without* the math background), I was more interested in understanding the overall picture about things like how ranking systems work or how recommendation engines are structured. While there was more detail than I needed (or understood), I still felt as if I accomplished my goal. I have a much greater appreciation for what companies like Google and Amazon have done in building web applications that allow the knowledge and wisdom of groups to be gathered and applied to my own preferences.
Statistical programmers will probably find years of entertainment here. :) "Normal" programmers will expand their horizons, too.
Accessible introduction to complex topics August 17, 2007
Leo Dirac (Seattle, WA United States)
44 out of 46 found this review helpful
Segaran has done an excellent job of explaining complex algorithms and mathematical concepts with clear examples and code that is both easy to read and useful. His coding style in Python often reads as clearly as pseudo-code in algorithm books. The examples give real-world grounding to abstract concepts like collaborative filtering and bayesian classification.
My favorite part is how he shows us code (gives it to us!) that goes out into the world, grabs masses of data and does interesting things with it. The use of a hierarchical clustering algorithm to dig into people's intrinsic desires in life as expressed in zebo is worth the price of the book alone. The graph that shows a strong connection between "wife", "kids", and "home" but a different connection between "husband", "children", and "job" is IMHO just fascinating.
Gems like that make this book worth reading cover to cover. After that it can happily hang out on your shelf as a reference anytime you need to build something to mine user data and extract the wisdom of crowds.
Great roundup of machine learning techniques November 20, 2007
Roger Hsueh (Cerritos, CA)
3 out of 9 found this review helpful
This book has great examples that make the machine learning algorithms come alive.
Most chapters have instructions on how to hook up the code to web based APIs so you can get some real data to play with.
I hope there's more books like this.
Excellent Resource for Clustering Algorithoms and Other AI Algorithoms April 24, 2008
Robert Hudock (Washington, DC)
1 out of 1 found this review helpful
I use python as my primary programming language, when I ordered this book I was concerned it would be more about website design then AI algorithms (collective intelligence encompasses a subset of soft AI algorithms that draw upon information from various sources readily avaliable on the Internet, large document collections, etc.) I found the text to be readable with broad application in other areas including document classification systems for analyzing large amount of documents in the context of e-discovery. I would recommend this book to anyone using any-type of clustering process for review and analyzing documents and data. Taxonomic, clustering, neural networks, etc. are sold generally to the public as magic while in fact the concepts are readily accessible in this book.
The most accessible book on machine learning I've found September 5, 2007
Thomas Lockney (Lake Oswego, OR United States)
17 out of 17 found this review helpful
I first learned of this book just a few weeks ago, shortly before it was available. I immediately read the sample chapter on the publisher's website and was certain I had to get a hold of a copy.
I was not in the least bit disappointed with what I found. It has been quite a while since I've looked at any Python code (I'm more of a Ruby fan, personally), but the code is easy to follow and it's a simple matter to extract the basic concepts into any language.
I have spent quite a few years now watching the field of machine intelligence from the sidelines, occasionally reading the odd technical write up or wikipedia article, trying to wrap my brain around the basic ideas. The thing is, it's not clear to me that in some regards, it's not that complex. It's just that most of the existing books and articles are written for those immersed in the field. This book is not like that. It explains things in clear language that is easy to follow, using simplified examples and making excellent use of graphics to "show" you how it works.
If you really want to dig in deep, Segaran provides exercises at the end of each chapter and gives you an appendix full of mathematical formulas (the "pure" representation of the algorithms).
Finally, I should mention that the last chapter does what so many other technical books should but don't: it clearly summarizes everything he has shown you. He does this in a straightforward way so that you won't have to go searching through the book, rereading everything again, to put these techniques into practice.
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